Category Archive: Machine Learning

Nov 13

Practical Bayesian Optimization with Spearmint

Recently, I’ve become interested in using Gaussian Processes for hyperparameter optimization. Coincidentally, there’s a great  NIPS 2012 paper by Jasper Snoek, Hugo Larochelle, and Ryan Adams about that very topic. Thankfully, not only is the paper insightful, but they also have Python source code available, called “spearmint” (I guess they chose the name so that …

Continue reading »

Sep 30

Review of STAN: off-the-shelf Hamiltonian MCMC

Recently, some folks at Andrew Gelman’s research lab have released a new and exciting inference package called STAN.  STAN is designed to do MCMC inference “off-the-shelf”, given just observed data and a BUGS-like definition of the probabilistic model.  I’ve played around with STAN in some detail, and my high-level review is summarized here Good: Installation …

Continue reading »